Single-Life Learning Paradigm

Updated 6 December 2025

The Single-Life Learning Paradigm is a continuous, one-shot learning process where an agent adapts cumulatively over a single lifetime without resets or episodic reinitialization.
It employs consolidation mechanisms, dynamic expansion, and selective pruning to freeze critical knowledge while ensuring adaptability through forward and backward transfer.
Applications span supervised, reinforcement, and symbolic learning, achieving few-shot generalization and non-forgetting performance in complex, continually evolving environments.

The single-life learning paradigm constitutes a fundamental shift in machine learning methodology, emphasizing uninterrupted, cumulative adaptation by an agent over the course of a single lifetime, with no restarts, episodic resets, or external re-initialization. Unlike standard episodic or batch approaches, which learn via repeated trials or by aggregating diverse task datasets and may explicitly demarcate task boundaries, the single-life paradigm requires all knowledge acquisition, storage, transfer, and refinement to occur continually within one persistent instance. This model closely mirrors biological lifelong learning, supporting properties such as non-forgetting, forward and backward transfer, few-shot generalization, dynamic plasticity-stability control, and emergent abstraction capabilities.

1. Conceptual Foundations and Scope

The origins of the single-life paradigm are rooted in the recognition that traditional frameworks—batch, episodic, and multi-task learning—fail to fully capture the realities of ongoing adaptation and incremental skill accumulation. The core distinction lies in the absence of resets or explicit task demarcations: the learner receives a stream of data (tasks, experiences) sequentially and must accommodate them solely through internal mechanisms (Ling et al., 2019, Ling et al., 2021, Strannegård et al., 2019). All forms of “re-exposure” or “replay” are either absent or severely restricted; training is strictly “once-through” per sample, and each data point is seen only once, akin to the human experience.

Formally, the paradigm is instantiated by continually updating the learner’s model parameters and auxiliary consolidation states with each new task or experience, without global reinitialization or episodic rollback. This applies both in supervised domains (classification, regression, vision representation learning) (Ling et al., 2019, Han et al., 3 Dec 2025) and reinforcement learning (RL), where policies must be adapted entirely within a single extended trial (Chen et al., 2022, Keramati et al., 31 Jan 2025, Nehme et al., 2023).

2. Central Mechanisms: Consolidation, Expansion, and Plasticity-Stability

A defining feature is the consolidation mechanism: every model parameter $\theta_i$ is associated with a non-negative “consolidation strength” $b_i$ regulating its adaptability during subsequent learning (Ling et al., 2019). When new data or tasks arrive, the loss minimized is

$L(\theta) = L_t(\theta) + \sum_{i=1}^n b_i (\theta_i - \theta_i^{\text{target}})^2,$

where $L_t$ is the current data/task loss and $\theta_i^{\text{target}}$ anchors weights to previous values. By setting $b_i$ large for critical, previously learned parameters, the system freezes their values; setting $b_i \approx 0$ allows plastic adaptation.

Expansion and pruning augment this process: to accommodate novel tasks not well served by existing representations, the architecture can dynamically grow (adding neurons, filters, or entire submodules) or prune away seldom-used structures, always governed by the current $b_i$ profile. “Transfer links” are explicitly managed via task similarity functions $\text{sim}(T_j, T_k)$ , which determine the initialization and adaptability of reused features.

In neuroplasticity-inspired models (Strannegård et al., 2019), additional rules include immediate expansion of new nodes on misclassification (memorization), generalization by abstracting patterns, and forgetting based on usage statistics, with backpropagation fine-tuning shared parameters for continual competence.

3. Algorithmic Realizations and Variants

Unified frameworks proceed as follows at task arrival (Ling et al., 2019, Ling et al., 2021):

Optionally prune/free capacity.
Measure task similarity and allocate new units.
Initialize new weights via similarity-based projection.
Set consolidation strengths: old-task parameters are frozen ( $b_i \rightarrow \infty$ ), new/transfer weights are modulated according to task overlap.
Train to convergence using the regularized loss.
Optionally perform partial or full rehearsal (unfreeze $b_i$ ) for backward transfer.

Pseudocode for such an “UnifiedSingleLifeLearner” is presented in (Ling et al., 2019) (see data for enumerated steps).

Neural-symbolic approaches extend the paradigm to programmatic domains: a probabilistic programming framework models world transition dynamics as mixtures of conditionally activated laws, enabling the agent to infer executable symbolic models after a single, unguided episode (Khan et al., 14 Oct 2025). In these frameworks, the computation graph is dynamically routed through only the relevant laws, avoiding gradient proliferation.

RL instantiations frame each trial as an infinite-horizon trajectory without resets. Algorithms such as single-life soft-actor critic (SLSAC) (Keramati et al., 31 Jan 2025) and Q-weighted adversarial learning (QWALE) (Chen et al., 2022, Nehme et al., 2023) use distribution matching to guide online adaptation: rewards and exploration are shaped to preferentially steer the policy toward distributions observed in prior offline data, efficiently recovering from out-of-distribution states within the one-shot life.

4. Characteristic Properties and Evaluation Criteria

Empirical and theoretical analyses (Ling et al., 2019, Ling et al., 2021, Strannegård et al., 2019) establish the following single-life learning properties:

Continual Learning without Forgetting: By freezing weights after each task, performance on previously learned tasks is stably retained.
Forward Transfer: When task similarity is high, transfer links allow rapid adaptation, matching few-shot learning efficiency.
Backward Transfer: Joint rehearsal on shared data can retroactively refine older task performance.
Confusion Resolution: Local fine-tuning and expansion mechanism minimize confusion between tasks (quantified by confusion measures).
Graceful Forgetting: Selective de-consolidation enables adaptive forgetting when capacity is reached.

Metrics include retained accuracy on old tasks, sample efficiency on new tasks, post-refinement performance, confusion rates, and memory consumption.

In RL, key benchmarks assess:

Task success rates under novel dynamics (Chen et al., 2022)
Recovery speed from unfamiliar states
Success with masked/hidden goal information (ablation studies show QWALE maintains performance even with explicit goal features removed) (Nehme et al., 2023).

Symbolic world modeling protocols evaluate state ranking (identifying valid next states among distractors) and state fidelity (closeness of generated states to ground truth) (Khan et al., 14 Oct 2025).

Vision models trained on single-life egocentric streams demonstrate emergent cross-model alignment and generalization on downstream geometric tasks, even matching diverse web-scale data in critical benchmarks (Han et al., 3 Dec 2025).

5. Parallels to Biological and Human Learning

Frameworks explicitly draw analogies to human cognition and neuroplasticity (Ling et al., 2019, Ling et al., 2021, Strannegård et al., 2019):

Memory Loss: Decaying $b_i$ or aggressive pruning causes old knowledge to fade, imitates phenomena such as age-related memory degradation.
Savant Syndrome ("Rain Man"): Full freezing with no transfer disables abstraction, yielding rote memory but poor generalization.
Alzheimer’s Patterns: Strong consolidation on oldest weights and shrinkage of free capacity models the “Ribot gradient”—preservation of remote memory, erasure of recent events.
Sleep Deprivation: Failure to rehearse or mismatch in consolidation schedules accumulates interference and confusion.

These parallels inform the design of $b_i$ schedules and dynamic expansion policies to model a spectrum of memory phenotypes and stability-plasticity trade-offs.

6. Applications and Domain-Specific Implementations

Single-life learning frameworks are instantiated across domains:

Supervised Learning: Deep nets with per-weight consolidation and dynamic expansion for task sequences—implements continual, few-shot, and forward/backward transfer (Ling et al., 2019, Ling et al., 2021).
Reinforcement Learning: Physical manipulation, disaster robotics, scientific computation. RL agents complete tasks in novel environments “in one life”, leveraging QWALE and SLSAC for robust adaptation (Chen et al., 2022, Keramati et al., 31 Jan 2025, Nehme et al., 2023).
Numerical Solving: Adaptive Krylov subspace exploration accelerates GMRES convergence on large sparse matrices by online dimension selection in a single uninterrupted trial, yielding order-of-magnitude speedups (Keramati et al., 31 Jan 2025).
Symbolic Modeling: OneLife constructs programmatic world models from minimal unguided trajectories, outperforming baselines on compositional scenario coverage and long-range planning (Khan et al., 14 Oct 2025).
Vision Representation Learning: Models trained exclusively on continuous egocentric video from one “life” converge to highly aligned geometric priors and generalize competitively, documented by alignment scores and downstream probe accuracy (Han et al., 3 Dec 2025).
Experience-Driven AI: Self-evolving agents in complex simulated environments integrate experience exploration, memory structuring, skill abstraction, and knowledge internalization into persistent agent “lives” (Cai et al., 26 Aug 2025).

7. Open Challenges and Future Research Directions

Current research identifies several unresolved challenges:

Meta-learning and Automated Consolidation Policies: Determining optimal $b_i$ schedules for balancing retention and plasticity (Ling et al., 2019, Ling et al., 2021).
Scaling and Capacity Management: Efficient heuristics for expansion/pruning and replay in the face of large, open-ended lifetimes (Cai et al., 26 Aug 2025).
Robustness to Distributional Shift: Extending discriminative matching to settings with severe environmental or reward function changes (Nehme et al., 2023).
Symbolic-Autonomous Model Construction: Enabling program inference and modular representation from highly stochastic, sparse regimes (Khan et al., 14 Oct 2025).
Interpretability and Skill Lifecycle Management: Managing emergent skills, memory indexing, and proactive adaptation without loss of transparency (Cai et al., 26 Aug 2025).
Generalization to High-Dimensional Inputs: Scalability of single-life learning in vision, language, and multi-modal domains remains an open frontier (Han et al., 3 Dec 2025, Cai et al., 26 Aug 2025).

A plausible implication is that increasing maturity in single-life learning architectures will drive unification of continual learning, transfer, and autonomous adaptation, with direct relevance for building robust, efficient AI systems capable of open-ended operation in complex, unstructured environments.

References

Ling and Bohn, "A Conceptual Framework for Lifelong Learning" (Ling et al., 2019)
Ling and Bohn, "A Deep Learning Framework for Lifelong Machine Learning" (Ling et al., 2021)
J. Li, "Some Insights into Lifelong Reinforcement Learning Systems" (Li, 2020)
M. Rosset, "Lifelong Learning Starting From Zero" (Strannegård et al., 2019)
S. Simsekli et al., "You Only Live Once: Single-Life Reinforcement Learning" (Chen et al., 2022)
F. Shahed, et al., "AK-SLRL: Adaptive Krylov Subspace Exploration Using Single-Life RL" (Keramati et al., 31 Jan 2025)
R. Patel et al., "Enhancing Robotic Manipulation: Harnessing ... Single-Life RL" (Nehme et al., 2023)
C. Wang et al., "One Life to Learn: Inferring Symbolic World Models ..." (Khan et al., 14 Oct 2025)
J. Zhang et al., "Building Self-Evolving Agents via Experience-Driven Lifelong Learning" (Cai et al., 26 Aug 2025)
K. Epstein et al., "Unique Lives, Shared World: Learning from Single-Life Videos" (Han et al., 3 Dec 2025)